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Andreas Tsouchlos
2022-12-09 14:59:01 +01:00
parent 3938c4aa31
commit 122aa848ab
39 changed files with 0 additions and 8192 deletions
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28
sw/cpp/.clang-format
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BasedOnStyle: LLVM
Language: Cpp
IndentWidth: 4
UseTab: Never
#NamespaceIndentation: All
PointerAlignment: Left
AccessModifierOffset: -4
AlwaysBreakTemplateDeclarations: true
LambdaBodyIndentation: Signature
MaxEmptyLinesToKeep: 3
# ColumnLimit: 128
CompactNamespaces: true
FixNamespaceComments: true
AllowShortFunctionsOnASingleLine: false
AllowShortIfStatementsOnASingleLine: true
AlignConsecutiveAssignments: true
AlignConsecutiveBitFields: true
AlignConsecutiveDeclarations: true
AlignConsecutiveMacros: true
#BraceWrapping:
# BeforeElse: true

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sw/cpp/CMakeLists.txt
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cmake_minimum_required (VERSION 3.0)
project(cpp_decoders)
if(NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
message(STATUS "Setting build type to 'Release' as none was specified.")
set(CMAKE_BUILD_TYPE
Release
CACHE STRING "Choose the type of build." FORCE)
set_property(
CACHE CMAKE_BUILD_TYPE
PROPERTY STRINGS
"Debug"
"Release")
endif()
set(CMAKE_CXX_STANDARD 23)
find_package(Eigen3 3.3 REQUIRED NO_MODULE)
find_package(pybind11 CONFIG REQUIRED)
include_directories(${pybind11_INCLUDE_DIRS})
find_package(OpenMP REQUIRED)
#add_compile_options(-ffast-math)
pybind11_add_module(cpp_decoders src/python_interface.cpp)
target_link_libraries(cpp_decoders PRIVATE Eigen3::Eigen OpenMP::OpenMP_CXX)
set(INSTALL_DIR ${CMAKE_CURRENT_SOURCE_DIR}/../cpp_modules)
install(TARGETS cpp_decoders ARCHIVE DESTINATION ${INSTALL_DIR}
LIBRARY DESTINATION ${INSTALL_DIR}
RUNTIME DESTINATION ${INSTALL_DIR})

277
sw/cpp/src/proximal.h
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#pragma once
#include <Eigen/Dense>
#include <bit>
#include <cstdlib>
#include <iostream>
#include <stdexcept>
#include <pybind11/eigen.h>
#include <pybind11/stl.h>
/*
*
* Using declarations
*
*/
template <int t_rows, int t_cols>
using MatrixiR = Eigen::Matrix<int, t_rows, t_cols, Eigen::RowMajor>;
template <int t_rows, int t_cols>
using MatrixdR = Eigen::Matrix<double, t_rows, t_cols, Eigen::RowMajor>;
template <int t_size>
using RowVectori = Eigen::RowVector<int, t_size>;
template <int t_size>
using RowVectord = Eigen::RowVector<double, t_size>;
/*
*
* Proximal decoder implementation
*
*/
/**
* @brief Class implementing the Proximal Decoding algorithm. See "Proximal
* Decoding for LDPC Codes" by Tadashi Wadayama, and Satoshi Takabe.
* @tparam t_m Number of rows of the H Matrix
* @tparam t_n Number of columns of the H Matrix
*/
template <int t_m, int t_n>
class ProximalDecoder {
public:
/**
* @brief Constructor
* @param H Parity-Check Matrix
* @param K Number of iterations to run while decoding
* @param omega Step size
* @param gamma Positive constant. Arises in the approximation of the prior
* PDF
* @param eta Positive constant slightly larger than one. See 3.2, p. 3
*/
ProximalDecoder(const Eigen::Ref<const MatrixiR<t_m, t_n>>& H, int K,
double omega, double gamma, double eta)
: mK(K), mOmega(omega), mGamma(gamma), mEta(eta), mH(H),
mH_zero_indices(find_zero(H)) {
Eigen::setNbThreads(8);
}
/**
* @brief Decode a received signal. The algorithm is detailed in 3.2, p.3.
* This function assumes a BPSK modulated signal and an AWGN channel.
* @param y Vector of received values. (y = x + w, where 'x' is element of
* [-1, 1]^n and 'w' is noise)
* @return \b std::pair of the form (x_hat, num_iter), x_hat is the most
* probably sent codeword and num_iter is the number of iterations that were
* performed. If the parity check fails and no valid codeword is reached,
* num_iter is -1
*/
std::pair<RowVectori<t_n>, int>
decode(const Eigen::Ref<const RowVectord<t_n>>& y) {
if (y.size() != mH.cols())
throw std::runtime_error("Length of vector must match H matrix");
RowVectord<t_n> s = RowVectord<t_n>::Zero(t_n);
RowVectori<t_n> x_hat;
RowVectord<t_n> r;
for (std::size_t i = 0; i < mK; ++i) {
r = s - mOmega * L_awgn(s, y);
s = projection(r - mGamma * grad_H(r));
x_hat = s.unaryExpr([](double d) {
uint64_t bits = std::bit_cast<uint64_t>(d);
// Return the sign bit: 1 for negative, 0 for positive
return (bits >> 63);
}).template cast<int>();
if (check_parity(x_hat)) {
return {x_hat, i + 1};
}
}
return {x_hat, -1};
}
/**
* @brief Decode a received signal an measure error value (x - sign(x_hat))
* @param x Transmitted word
* @param y Received signal
* @return \b std::vector of error values. Each element corresponds to one
* iteration of the algorithm
*/
std::vector<double>
get_error_values(const Eigen::Ref<const RowVectori<t_n>>& x,
const Eigen::Ref<const RowVectord<t_n>>& y) {
if (y.size() != mH.cols())
throw std::runtime_error("Length of vector must match H matrix");
std::vector<double> error_values;
error_values.reserve(mK);
RowVectord<t_n> s = RowVectord<t_n>::Zero(t_n);
RowVectori<t_n> x_hat;
RowVectord<t_n> r;
for (std::size_t i = 0; i < mK; ++i) {
r = s - mOmega * L_awgn(s, y);
s = projection(r - mGamma * grad_H(r));
x_hat = s.unaryExpr([](double d) {
uint64_t bits = std::bit_cast<uint64_t>(d);
// Return the sign bit: 1 for negative, 0 for positive
return (bits >> 63);
}).template cast<int>();
RowVectord<t_n> x_hat_bpsk =
-1 * ((2 * x_hat.template cast<double>()).array() - 1).matrix();
error_values.push_back(
(x.template cast<double>() - x_hat_bpsk).norm());
if (check_parity(x_hat)) {
break;
}
}
return error_values;
}
/**
* @brief Decode a received signal and measure the norm of the two gradients
* at each iteration
* @param y
* @return \b std::vector of \b std::pair of gradient values. Each element corresponds to
* one iteration. Result is of the form [(grad_H_1, grad_L_1), ...]
*/
std::vector<std::pair<double, double>>
get_gradient_values(const Eigen::Ref<const RowVectord<t_n>>& y) {
if (y.size() != mH.cols())
throw std::runtime_error("Length of vector must match H matrix");
std::vector<std::pair<double, double>> gradient_values;
gradient_values.reserve(mK);
RowVectord<t_n> s = RowVectord<t_n>::Zero(t_n);
RowVectori<t_n> x_hat;
RowVectord<t_n> r;
for (std::size_t i = 0; i < mK; ++i) {
RowVectord<t_n> gradl = L_awgn(s, y);
r = s - mOmega * gradl;
RowVectord<t_n> gradh = grad_H(r);
s = projection(r - mGamma * gradh);
gradient_values.push_back({gradh.norm(), gradl.norm()});
x_hat = s.unaryExpr([](double d) {
uint64_t bits = std::bit_cast<uint64_t>(d);
// Return the sign bit: 1 for negative, 0 for positive
return (bits >> 63);
}).template cast<int>();
if (check_parity(x_hat)) {
break;
}
}
return gradient_values;
}
/**
* @brief Get the values of all member variables necessary to recreate an
* exact copy of this class. Used for pickling
* @return \b std::tuple
*/
auto get_decoder_state() const {
return std::tuple(mH, mK, mOmega, mGamma, mEta);
}
private:
const int mK;
const double mOmega;
const double mGamma;
const double mEta;
const MatrixiR<t_m, t_n> mH;
const std::vector<Eigen::Index> mH_zero_indices;
/**
* Variation of the negative log-likelihood for the special case of AWGN
* noise. See 4.1, p. 4.
*/
static Eigen::RowVectorXd L_awgn(const RowVectord<t_n>& s,
const RowVectord<t_n>& y) {
return s.array() - y.array();
}
/**
* @brief Find all indices of a matrix, where the corresponding value is
* zero
* @return \b std::vector of indices
*/
static std::vector<Eigen::Index> find_zero(MatrixiR<t_m, t_n> mat) {
std::vector<Eigen::Index> indices;
for (Eigen::Index i = 0; i < mat.size(); ++i)
if (mat(i) == 0) indices.push_back(i);
return indices;
}
/**
* Gradient of the code-constraint polynomial. See 2.3, p. 2.
*/
RowVectord<t_n> grad_H(const RowVectord<t_n>& x) {
MatrixdR<t_m, t_n> A_prod_matrix = x.replicate(t_m, 1);
for (const auto& index : mH_zero_indices)
A_prod_matrix(index) = 1;
RowVectord<t_m> A_prods = A_prod_matrix.rowwise().prod();
RowVectord<t_m> B_terms =
(A_prods.array().pow(2) - A_prods.array()).matrix().transpose();
RowVectord<t_n> B_sums = B_terms * mH.template cast<double>();
RowVectord<t_n> result = 4 * (x.array().pow(2) - 1) * x.array() +
(2 * x.array().inverse()) * B_sums.array();
return result;
}
/**
* Perform a parity check for a given codeword.
* @param x_hat: codeword to be checked (element of [0, 1]^n)
* @return \b True if the parity check passes, i.e. the codeword is valid.
* False otherwise
*/
bool check_parity(const RowVectori<t_n>& x_hat) {
RowVectori<t_m> syndrome =
(mH * x_hat.transpose()).unaryExpr([](int i) { return i % 2; });
return !(syndrome.count() > 0);
}
/**
* Project a vector onto [-eta, eta]^n in order to avoid numerical
* instability. Detailed in 3.2, p. 3 (Equation (15)).
* @param v Vector to project
* @return v clipped to [-eta, eta]^n
*/
RowVectord<t_n> projection(const RowVectord<t_n>& v) {
return v.cwiseMin(mEta).cwiseMax(-mEta);
}
};

58
sw/cpp/src/python_interface.cpp
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#define EIGEN_STACK_ALLOCATION_LIMIT 1048576
#include "proximal.h"
#include <pybind11/pybind11.h>
namespace py = pybind11;
using namespace pybind11::literals;
#define DEF_PROXIMAL_DECODER(name, m, n) \
py::class_<ProximalDecoder<m, n>>(proximal, name) \
.def(py::init<MatrixiR<m, n>, int, double, double, double>(), \
"H"_a.noconvert(), "K"_a = 100, "omega"_a = 0.0002, \
"gamma"_a = .05, "eta"_a = 1.5) \
.def("decode", &ProximalDecoder<m, n>::decode, "y"_a.noconvert()) \
.def("get_error_values", &ProximalDecoder<m, n>::get_error_values, \
"x"_a.noconvert(), "y"_a.noconvert()) \
.def("get_gradient_values", \
&ProximalDecoder<m, n>::get_gradient_values, "y"_a.noconvert()) \
.def(py::pickle( \
[](const ProximalDecoder<m, n>& a) { \
auto state = a.get_decoder_state(); \
\
MatrixiR<m, n> H = std::get<0>(state); \
int K = std::get<1>(state); \
double omega = std::get<2>(state); \
double gamma = std::get<3>(state); \
double eta = std::get<4>(state); \
\
return py::make_tuple(H, K, omega, gamma, eta); \
}, \
[](py::tuple t) { \
return ProximalDecoder<m, n>{ \
t[0].cast<MatrixiR<m, n>>(), t[1].cast<int>(), \
t[2].cast<double>(), t[3].cast<double>(), \
t[4].cast<double>()}; \
}));
PYBIND11_MODULE(cpp_decoders, proximal) {
proximal.doc() = "Proximal decoder";
DEF_PROXIMAL_DECODER("ProximalDecoder_7_3", 3, 7)
DEF_PROXIMAL_DECODER("ProximalDecoder_31_20", 20, 31)
DEF_PROXIMAL_DECODER("ProximalDecoder_31_5", 5, 31)
DEF_PROXIMAL_DECODER("ProximalDecoder_96_48", 48, 96)
DEF_PROXIMAL_DECODER("ProximalDecoder_204_102", 102, 204)
DEF_PROXIMAL_DECODER("ProximalDecoder_408_204", 204, 408)
DEF_PROXIMAL_DECODER("ProximalDecoder_Dynamic", Eigen::Dynamic,
Eigen::Dynamic)
py::register_exception<std::runtime_error>(
proximal, "CppException: std::runtime_error");
py::register_exception<std::bad_alloc>(proximal,
"CppException: std::bad_alloc");
}

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sw/python/README.md
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# Bachelorarbeit
## Software
Warning: At least a `Python 3.9` or higher is required to properly run the
scripts in this project
### Create a python virtual environment
In the root directory of this repository run
```bash
$ python3 -m venv venv
$ source venv/bin/activate
$ pip3 install -r requirements.txt
```
### Run the unit tests
In the root directory of this repository run
```bash
$ python -m unittest discover test
```

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sw/python/cpp_modules/__init__.py
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sw/python/cpp_modules/cpp_decoders.cpython-310-x86_64-linux-gnu.so
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sw/python/decoders/__init__.py
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"""This package contains a number of different decoder implementations for
LDPC codes."""

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sw/python/decoders/maximum_likelihood.py
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import numpy as np
import itertools
class MLDecoder:
"""This class naively implements a soft decision decoder. The decoder
calculates the correlation between the received signal and each codeword
and then chooses the one with the largest correlation.
"""
def __init__(self, G: np.array, H: np.array):
"""Construct a new SoftDecisionDecoder object.
:param G: Generator matrix
:param H: Parity check matrix
"""
self._G = G
self._H = H
self._datawords, self._codewords = self._gen_codewords()
# The codewords, but mapped to [-1, 1]^n
self._codewords_bpsk = 1 - 2 * self._codewords
def _gen_codewords(self) -> np.array:
"""Generate a list of all possible codewords.
:return: Numpy array of the form [[codeword_1], [codeword_2], ...]
(Each generated codeword is an element of [0, 1]^n)
"""
k, n = self._G.shape
# Generate a list of all possible data words
u_lst = [list(i) for i in itertools.product([0, 1], repeat=k)]
u_lst = np.array(u_lst)
# Map each data word onto a codeword
c_lst = np.dot(u_lst, self._G) % 2
return u_lst, c_lst
def decode(self, y: np.array) -> np.array:
"""Decode a received signal.
This function assumes a BPSK modulated signal.
:param y: Vector of received values. (y = x + w, where 'x' is
element of [-1, 1]^n and 'w' is noise)
:return: Most probably sent codeword (element of [0, 1]^k)
"""
correlations = np.dot(self._codewords_bpsk, y)
return self._codewords[np.argmax(correlations)]

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sw/python/decoders/proximal.py
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import numpy as np
class ProximalDecoder:
"""Class implementing the Proximal Decoding algorithm. See "Proximal
Decoding for LDPC Codes"
by Tadashi Wadayama, and Satoshi Takabe.
"""
def __init__(self, H: np.array, K: int = 1000, omega: float = 0.0002,
gamma: float = 0.05, eta: float = 1.5):
"""Construct a new ProximalDecoder Object.
:param H: Parity Check Matrix
:param K: Max number of iterations to perform when decoding
:param omega: Step size for the gradient descent process
:param gamma: Positive constant. Arises in the approximation of the
prior PDF
:param eta: Positive constant slightly larger than one. See 3.2, p. 3
"""
self._H = H
self._K = K
self._step_size = omega
self._gamma = gamma
self._eta = eta
self._k, self._n = self._H.shape
self._H_ne_0 = H != 0
@staticmethod
def _L_awgn(s: np.array, y: np.array) -> np.array:
"""Variation of the negative log-likelihood for the special case of
AWGN noise. See 4.1, p. 4.
"""
return s - y
def _grad_h(self, x: np.array) -> np.array:
"""Gradient of the code-constraint polynomial. See 2.3, p. 2."""
# Pre-computations
A_prod_matrix = np.tile(x, (self._k, 1))
A_prods = np.prod(A_prod_matrix, axis=1, where=self._H_ne_0)
# Calculate gradient
sums = np.dot(A_prods ** 2 - A_prods, self._H)
result = 4 * (x ** 2 - 1) * x + (2 / x) * sums
return result
def _projection(self, v):
"""Project a vector onto [-eta, eta]^n in order to avoid numerical
instability. Detailed in 3.2, p. 3 (Equation (15)).
:param v: Vector to project
:return: x clipped to [-eta, eta]^n
"""
return np.clip(v, -self._eta, self._eta)
def _check_parity(self, x_hat: np.array) -> bool:
"""Perform a parity check for a given codeword.
:param x_hat: codeword to be checked (element of [0, 1]^n)
:return: True if the parity check passes, i.e. the codeword is
valid. False otherwise
"""
syndrome = np.dot(self._H, x_hat) % 2
return not np.any(syndrome)
def decode(self, y: np.array) -> np.array:
"""Decode a received signal. The algorithm is detailed in 3.2, p.3.
This function assumes a BPSK modulated signal and an AWGN channel.
:param y: Vector of received values. (y = x + w, where 'x' is
element of [-1, 1]^n and 'w' is noise)
:return: Most probably sent codeword (element of [0, 1]^n). If
decoding fails, the returned value is 'None'
"""
s = np.zeros(self._n)
x_hat = np.zeros(self._n)
for k in range(self._K):
r = s - self._step_size * self._L_awgn(s, y)
s = r - self._gamma * self._grad_h(r)
s = self._projection(s) # Equation (15)
x_hat = np.sign(s)
# Map the codeword from [ -1, 1]^n to [0, 1]^n
x_hat = (x_hat == -1) * 1
if self._check_parity(x_hat):
return x_hat
return None

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sw/python/requirements.txt
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matplotlib==3.6.2
pandas==1.5.2
seaborn==0.12.1
tqdm==4.64.1

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6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
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19 107 175 310 380 489 0
25 149 180 322 384 480 0
68 124 230 323 366 437 501
69 121 231 323 339 472 0
23 150 223 266 404 490 0
70 151 219 258 352 451 0
50 117 232 267 405 470 0
71 146 223 249 371 475 0
9 152 233 274 406 491 0
53 86 234 324 388 432 0
34 153 234 251 346 429 0
42 154 235 282 358 490 0
23 155 176 325 390 467 0
72 156 236 286 347 492 0
58 157 218 296 401 434 0
73 158 220 300 401 491 0
8 159 198 284 407 493 0
74 104 181 298 370 475 0
59 126 237 280 408 494 0
75 149 189 261 376 490 0
69 129 186 295 400 470 0
76 90 228 326 409 495 0
36 101 238 285 403 439 0
52 122 202 327 410 496 0
72 112 226 256 377 486 0
63 108 212 259 411 497 0
27 142 239 325 393 460 0
46 158 236 272 404 477 0
57 159 227 282 412 484 0
62 88 229 328 384 498 0
36 160 240 329 345 479 0
44 119 206 322 365 394 0
58 99 206 304 367 456 0
38 153 192 271 347 493 0
77 157 241 330 413 499 0
45 161 237 331 376 500 0
77 101 190 313 414 438 0
70 146 186 274 415 447 0
78 162 242 317 416 501 0
79 163 198 311 408 443 0
16 115 171 325 416 494 0
7 141 243 279 416 486 0
4 107 244 315 407 500 0
71 160 194 332 417 499 0
44 153 245 279 403 484 0
30 97 176 328 389 395 0
80 128 246 312 359 447 0
15 162 237 326 394 457 0
80 164 211 321 392 465 0
68 165 190 307 356 463 0
57 141 210 283 340 464 0
29 165 220 333 415 498 0
74 144 242 329 418 471 0
60 154 185 289 395 501 0
54 124 183 321 369 502 0
26 165 192 331 353 435 0
65 125 213 327 363 492 0
14 155 225 263 379 450 0
49 166 247 324 337 477 0
30 161 248 287 417 503 0
45 93 171 268 373 468 0
78 152 191 319 419 454 0
81 155 224 288 375 487 0
41 142 191 270 339 474 0
65 152 240 273 390 459 0
35 87 207 314 410 436 0
39 85 249 334 411 458 0
49 102 238 267 420 492 0
48 138 215 335 360 452 0
59 123 246 262 344 419 0
4 109 233 305 335 426 0
50 133 244 323 354 483 0
61 120 179 299 410 467 0
43 148 173 286 421 504 0
82 154 250 322 402 489 0
71 116 235 301 346 466 0
79 120 251 319 402 482 0
5 140 236 336 400 473 0
83 163 204 306 365 453 0
15 148 200 270 383 473 0
10 87 249 337 342 443 0
9 147 252 316 374 449 0
73 86 201 314 422 463 0
48 119 203 291 345 441 0
55 149 253 277 414 487 0
17 116 254 269 397 504 0
64 90 224 318 413 483 0
14 143 212 303 423 482 0
52 137 177 265 411 438 0
61 132 216 255 413 425 0
82 135 218 276 386 435 0
80 114 244 324 423 0 0
78 94 245 330 333 503 0
5 157 248 291 407 478 0
3 108 226 313 417 491 0
1 92 243 328 424 504 0
67 140 232 338 382 454 0
35 113 238 292 408 471 0
46 104 180 264 406 465 0
20 150 204 327 348 445 0
18 138 253 260 423 495 0
60 113 211 298 409 496 0
55 159 194 316 388 476 0
11 123 209 334 421 449 0
82 96 168 284 350 397 0
75 164 195 306 415 489 0
74 166 222 278 368 457 0
2 160 246 304 391 488 0
28 125 241 260 350 461 0
76 110 233 299 382 485 0
84 161 231 308 371 418 0
64 167 214 315 381 485 0
37 130 222 334 406 452 0
81 145 208 254 418 462 0
27 167 255 275 405 469 0
11 106 247 332 399 440 0
66 118 178 335 396 499 0
79 127 239 269 385 391 0
54 168 215 281 404 459 0
84 127 229 330 412 497 0
6 151 227 338 378 436 0
83 109 230 287 421 481 0
26 112 178 255 409 419 0
40 167 182 333 399 502 0
53 156 184 302 386 464 0
77 145 170 311 359 498 0
12 163 235 257 425 455 0
81 106 230 292 380 424 0
84 117 213 310 392 0 0
31 89 252 331 420 496 0
56 115 216 290 378 440 0
13 147 247 318 387 494 0
66 139 234 329 381 0 0
69 156 240 309 422 503 0
34 135 231 275 398 495 0
17 162 169 265 405 493 0
70 91 242 283 364 430 0
75 85 251 320 355 476 0
68 151 250 320 420 462 0
63 139 228 296 349 446 0
51 150 243 293 414 468 0
76 158 239 294 351 444 0
67 166 183 309 383 497 0
31 164 196 254 357 458 481
7 132 232 268 357 450 0
37 168 245 336 379 500 0
73 136 170 332 370 429 0
72 105 187 326 369 424 0
24 110 248 337 412 502 0
83 169 241 303 393 488 0

154
sw/python/scripts/proximal/simulate_2d_BER.py
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@@ -1,154 +0,0 @@
import sys, os
import sys, os
sys.path.append(os.path.abspath('../..'))
print(sys.path)
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import signal
from timeit import default_timer
from functools import partial
from utility import codes, noise, misc
from utility.simulation.simulators import GenericMultithreadedSimulator
from utility.simulation import SimulationManager
from cpp_modules.cpp_decoders import ProximalDecoder_204_102 as ProximalDecoder
def task_func(params):
"""Function called by the GenericMultithreadedSimulator instance.
Calculate the BER, FER, and DFR for a given SNR and gamma.
"""
signal.signal(signal.SIGINT, signal.SIG_IGN)
decoder = params["decoder"]
max_iterations = params["max_iterations"]
SNR = params["SNR"]
n = params["n"]
k = params["k"]
c = np.zeros(n)
x_bpsk = c + 1
total_bit_errors = 0
total_frame_errors = 0
dec_fails = 0
num_iterations = 0
for i in range(max_iterations):
x = noise.add_awgn(x_bpsk, SNR, n, k)
x_hat, k_max = decoder.decode(x)
bit_errors = misc.count_bit_errors(x_hat, c)
if bit_errors > 0:
total_bit_errors += bit_errors
total_frame_errors += 1
num_iterations += 1
if k_max == -1:
dec_fails += 1
if total_frame_errors > 100:
break
BER = total_bit_errors / (num_iterations * n)
FER = total_frame_errors / num_iterations
DFR = dec_fails / (num_iterations + dec_fails)
return {"BER": BER, "FER": FER, "DFR": DFR,
"num_iterations": num_iterations}
def get_params(code_name: str):
"""In this function all parameters for the simulation are defined."""
# Define global simulation parameters
H_file = f"../../res/{code_name}.alist"
H = codes.read_alist_file(H_file)
n_min_k, n = H.shape
k = n - n_min_k
omega = 0.05
K = 100
gammas = np.arange(0.0, 0.17, 0.01)
SNRs = np.arange(1, 6, 0.5)
max_iterations = 20000
# Define parameters different for each task
task_params = []
for i, SNR in enumerate(SNRs):
for j, gamma in enumerate(gammas):
decoder = ProximalDecoder(H=H.astype('int32'), K=K, omega=omega,
gamma=gamma)
task_params.append(
{"decoder": decoder, "max_iterations": max_iterations,
"SNR": SNR, "gamma": gamma, "n": n, "k": k})
return omega, K, task_params
def configure_new_simulation(sim_mgr: SimulationManager, code_name: str,
sim_name: str) -> None:
sim = GenericMultithreadedSimulator()
omega, K, task_params = get_params(code_name)
sim.task_params = task_params
sim.task_func = task_func
sim.format_func = partial(misc.pgf_reformat_data_3d, x_param_name="SNR",
y_param_name="gamma",
z_param_names=["BER", "FER", "DFR",
"num_iterations"])
sim_mgr.configure_simulation(simulator=sim, name=sim_name,
additional_metadata={"omega": omega, "K": K})
def main():
# code_name = "BCH_7_4"
# code_name = "BCH_31_11"
# code_name = "BCH_31_26"
# code_name = "96.3.965"
# code_name = "204.33.486"
code_name = "204.33.484"
# code_name = "204.55.187"
# code_name = "408.33.844"
sim_name = f"2d_BER_FER_DFR_{misc.slugify(code_name)}"
# Run simulation
sim_mgr = SimulationManager(saves_dir="sim_saves",
results_dir="sim_results")
unfinished_sims = sim_mgr.get_unfinished()
if len(unfinished_sims) > 0:
sim_mgr.load_unfinished(unfinished_sims[0])
else:
configure_new_simulation(sim_mgr=sim_mgr, code_name=code_name,
sim_name=sim_name)
sim_mgr.simulate()
# Plot results
sns.set_theme()
ax = sns.lineplot(data=sim_mgr.get_current_results(), x="SNR", y="BER",
hue="gamma")
ax.set_yscale('log')
ax.set_ylim((5e-5, 2e-0))
plt.show()
if __name__ == "__main__":
main()

147
sw/python/scripts/proximal/simulate_2d_avg_error.py
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@@ -1,147 +0,0 @@
import sys, os
sys.path.append(os.path.abspath('../..'))
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import signal
from timeit import default_timer
from functools import partial
import pandas as pd
from utility import codes, noise, misc
from utility.simulation.simulators import GenericMultithreadedSimulator
from utility.simulation import SimulationManager
from cpp_modules.cpp_decoders import ProximalDecoder_204_102 as ProximalDecoder
def task_func(params):
"""Function called by the GenericMultithreadedSimulator instance.
Calculate the average error over a number of iterations.
"""
signal.signal(signal.SIGINT, signal.SIG_IGN)
decoder = params["decoder"]
num_iterations = params["num_iterations"]
x_bpsk = params["x_bpsk"]
SNR = params["SNR"]
n = params["n"]
k = params["k"]
K = params["K"]
avg_error_values = np.zeros(K)
for i in range(num_iterations):
x = noise.add_awgn(x_bpsk, SNR, n, k)
error_values = decoder.get_error_values(x_bpsk.astype('int32'), x)
for j, val in enumerate(error_values):
avg_error_values[j] += val
avg_error_values = avg_error_values / num_iterations
return {"err": avg_error_values}
def get_params(code_name: str):
"""In this function all parameters for the simulation are defined."""
# Define global simulation parameters
H_file = f"../../res/{code_name}.alist"
H = codes.read_alist_file(H_file)
n_min_k, n = H.shape
k = n - n_min_k
SNR = 8
omegas = np.logspace(-0, -10, 40)
K = 200
num_iterations = 1000
x_bpsk = np.zeros(n) + 1
# Define parameters different for each task
task_params = []
for i, omega in enumerate(omegas):
decoder = ProximalDecoder(H=H.astype('int32'), K=K,
omega=omega)
task_params.append(
{"decoder": decoder, "num_iterations": num_iterations,
"x_bpsk": x_bpsk, "SNR": SNR, "n": n, "k": k, "K": K,
"omega": omega})
return SNR, K, task_params
def reformat_data(results):
"""Reformat the data obtained from the GenericMultithreadedSimulator to
be usable by pgfplots.
"""
K = 200
num_points = len(results) * K
x = np.zeros(num_points)
y = np.zeros(num_points)
z = np.zeros(num_points)
for i, (params, result) in enumerate(results.items()):
np.put(x, np.arange(i * K, (i + 1) * K), np.arange(1, K+1))
np.put(y, np.arange(i * K, (i + 1) * K), params["omega"])
np.put(z, np.arange(i * K, (i + 1) * K), result["err"])
x = x[::4]
y = y[::4]
z = z[::4]
df = pd.DataFrame({"k": x, "omega": y, "err": z}).sort_values(
by=['k', 'omega'], ascending=[True, False])
return df
def configure_new_simulation(sim_mgr: SimulationManager, code_name: str,
sim_name: str) -> None:
sim = GenericMultithreadedSimulator()
SNR, K, task_params = get_params(code_name)
sim.task_params = task_params
sim.task_func = task_func
sim.format_func = reformat_data
sim_mgr.configure_simulation(simulator=sim, name=sim_name,
additional_metadata={"SNR": SNR, "K": K})
def main():
# code_name = "BCH_7_4"
# code_name = "BCH_31_11"
# code_name = "BCH_31_26"
# code_name = "96.3.965"
# code_name = "204.33.486"
code_name = "204.33.484"
# code_name = "204.55.187"
# code_name = "408.33.844"
sim_name = f"2d_avg_error_{misc.slugify(code_name)}"
# Run simulation
sim_mgr = SimulationManager(saves_dir="sim_saves",
results_dir="sim_results")
unfinished_sims = sim_mgr.get_unfinished()
if len(unfinished_sims) > 0:
sim_mgr.load_unfinished(unfinished_sims[0])
else:
configure_new_simulation(sim_mgr=sim_mgr, code_name=code_name,
sim_name=sim_name)
sim_mgr.simulate()
if __name__ == "__main__":
main()

88
sw/python/scripts/proximal/simulate_gradient.py
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@@ -1,88 +0,0 @@
import sys, os
sys.path.append(os.path.abspath('../..'))
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import signal
from timeit import default_timer
from tqdm import tqdm
from utility import codes, noise, misc
from utility.simulation.simulators import GenericMultithreadedSimulator
# from cpp_modules.cpp_decoders import ProximalDecoder
from cpp_modules.cpp_decoders import ProximalDecoder_204_102 as ProximalDecoder
def simulate(H_file, SNR, omega, K, gamma):
H = codes.read_alist_file(f"../../res/{H_file}")
n_min_k, n = H.shape
k = n - n_min_k
decoder = ProximalDecoder(H.astype('int32'), K=K, omega=omega, gamma=gamma)
c = np.zeros(n)
x_bpsk = (c + 1)
avg_grad_values = np.zeros(shape=(K, 2))
for i in range(1000):
x = noise.add_awgn(x_bpsk, SNR, n, k)
grad_values = decoder.get_gradient_values(x)
for j, (val_h, val_l) in enumerate(grad_values):
avg_grad_values[j, 0] += val_h
avg_grad_values[j, 1] += val_l
avg_grad_values = avg_grad_values / 1000
return avg_grad_values
def reformat_data(results):
return pd.DataFrame({"k": np.arange(0, results.size // 2, 1), "grad_h": results[:, 0], "grad_l": results[:, 1]})
def main():
# Set up simulation params
sim_name = "avg_grad_1dB"
# H_file = "96.3.965.alist"
H_file = "204.33.486.alist"
# H_file = "204.33.484.alist"
# H_file = "204.55.187.alist"
# H_file = "408.33.844.alist"
# H_file = "BCH_7_4.alist"
# H_file = "BCH_31_11.alist"
# H_file = "BCH_31_26.alist"
SNR = 1
omega = 0.05
K = 100
gamma = 0.05
# Run simulation
start_time = default_timer()
results = simulate(H_file, SNR, omega, K, gamma)
end_time = default_timer()
print(f"duration: {end_time - start_time}")
df = reformat_data(results)
df.to_csv(
f"sim_results/{sim_name}_{misc.slugify(H_file)}.csv", index=False)
sns.set_theme()
sns.lineplot(data=df, x="k", y="grad_h")
sns.lineplot(data=df, x="k", y="grad_l")
plt.show()
if __name__ == "__main__":
main()

2
sw/python/utility/__init__.py
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@@ -1,2 +0,0 @@
"""This package contains various utilities that can be used in combination
with the decoders."""

249
sw/python/utility/codes.py
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@@ -1,249 +0,0 @@
"""This file Helper functions for generating an H matrix from alist data.
Code from https://github.com/gnuradio/gnuradio/blob/master/gr-fec/python/fec
/LDPC/Generate_LDPC_matrix_functions.py
"""
import numpy as np
#
# Related to alist files
#
def _parse_alist_header(header):
size = header.split()
return int(size[0]), int(size[1])
def read_alist_file(filename):
"""
This function reads in an alist file and creates the
corresponding parity check matrix H. The format of alist
files is described at:
http://www.inference.phy.cam.ac.uk/mackay/codes/alist.html
"""
with open(filename, 'r') as myfile:
data = myfile.readlines()
numCols, numRows = _parse_alist_header(data[0])
H = np.zeros((numRows, numCols))
# The locations of 1s starts in the 5th line of the file
for lineNumber in np.arange(4, 4 + numCols):
indices = data[lineNumber].split()
for index in indices:
H[int(index) - 1, lineNumber - 4] = 1
return H
#
# G matrices of specific codes
#
# @formatter:off
Gs = {'Hamming_7_4': np.array([[1, 0, 0, 0, 0, 1, 1],
[0, 1, 0, 0, 1, 0, 1],
[0, 0, 1, 0, 1, 1, 0],
[0, 0, 0, 1, 1, 1, 1]]),
'Golay_24_12': np.array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1]]),
'BCH_15_7': np.array([[1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1],
[0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1],
[0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1]]),
'BCH_31_6': np.array([[1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1],
[0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0],
[0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1],
[0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1],
[0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1],
[0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1]]),
'BCH_31_11': np.array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1]]),
'BCH_31_16': np.array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1]]),
'BCH_31_21': np.array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1]]),
'BCH_63_16': np.array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1]]),
'BCH_63_30': np.array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1]]),
# TODO: Fix this. This code should be systematic
'BCH_63_45': np.array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1]])
}
# @formatter:on
#
# Utilities for systematic codes
#
def get_systematic_H(G: np.array) -> np.array:
"""Compute the H matrix for a systematic code.
:param G: Generator matrix of the systematic code
:return: Parity check matrix H
"""
k, n = G.shape
I = G[:, :k]
assert np.array_equal(I, np.identity(k))
P = G[:, k:]
H = np.zeros(shape=(n - k, n))
H[:, :k] = P.T
H[:, k:] = np.identity(n - k)
return H

72
sw/python/utility/misc.py
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@@ -1,72 +0,0 @@
import unicodedata
import re
import typing
import pandas as pd
import numpy as np
def slugify(value, allow_unicode=False):
"""
Taken from https://github.com/django/django/blob/master/django/utils
/text.py
Convert to ASCII if 'allow_unicode' is False. Convert spaces or repeated
dashes to single dashes. Remove characters that aren't alphanumerics,
underscores, or hyphens. Convert to lowercase. Also strip leading and
trailing whitespace, dashes, and underscores.
"""
value = str(value)
if allow_unicode:
value = unicodedata.normalize('NFKC', value)
else:
value = unicodedata.normalize('NFKD', value).encode('ascii',
'ignore').decode(
'ascii')
value = re.sub(r'[^\w\s-]', '', value.lower())
return re.sub(r'[-\s]+', '-', value).strip('-_')
def pgf_reformat_data_3d(results: typing.Sequence, x_param_name: str,
y_param_name: str,
z_param_names: typing.Sequence[str]):
"""Reformat the results obtained from the GenericMultithreadedSimulator
into a form usable by pgfplots.
:param results: Results from GenericMultiThreadedSimulator
(dict of the form {params1: results1, params2: results2, ...}),
where resultsN and paramsN are themselves dicts:
paramsN = {param_name_1: val, param_name_2: val, ...}
resultsN = {result_name_1: val, result_name_2: val, ...}
:param x_param_name:
:param y_param_name:
:param z_param_names:
:return: pandas DataFrame of the following form:
{x_param_name: [x1, x1, x1, ..., x2, x2, x2, ...],
y_param_name: [y1, y2, y3, ..., y1, y2, y3, ...],
z_param_name: [z11, z21, z31, ..., z12, z22, z32, ...]}
"""
# Create result variables
x = np.zeros(len(results))
y = np.zeros(len(results))
zs = {name: np.zeros(len(results)) for name in z_param_names}
# Populate result variables
for i, (params, result) in enumerate(results.items()):
x_val = params[x_param_name]
y_val = params[y_param_name]
for z_param_name in z_param_names:
zs[z_param_name][i] = result[z_param_name]
x[i] = x_val
y[i] = y_val
# Create and return pandas DataFrame
df = pd.DataFrame({x_param_name: x, y_param_name: y})
for z_param_name in z_param_names:
df[z_param_name] = zs[z_param_name]
return df.sort_values(by=[x_param_name, y_param_name])
def count_bit_errors(x: np.array, x_hat: np.array) -> int:
"""Count the number of different bits between two words."""
return np.sum(x != x_hat)

34
sw/python/utility/noise.py
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@@ -1,34 +0,0 @@
"""Utility functions relating to noise and SNR calculations."""
import numpy as np
def get_noise_variance_from_SNR(SNR: float, n: int, k: int) -> float:
"""Calculate the variance of the noise from an SNR and the signal
amplitude.
:param SNR: Signal-to-Noise-Ratio in dB (E_b/N_0)
:param n: Length of a codeword of the used code
:param k: Length of a dataword of the used code
:return: Variance of the noise
"""
SNR_linear = 10 ** (SNR / 10)
variance = 1 / (2 * (k / n) * SNR_linear)
return variance
def add_awgn(c: np.array, SNR: float, n: int, k: int) -> np.array:
"""Add Additive White Gaussian Noise to a data vector. As this function
adds random noise to the input, the output changes, even if it is called
multiple times with the same input.
:param c: Binary vector representing the data to be transmitted
:param SNR: Signal-to-Noise-Ratio in dB
:param n: Length of a codeword of the used code
:param k: Length of a dataword of the used code
:return: Data vector with added noise
"""
noise_var = get_noise_variance_from_SNR(SNR, n, k)
y = c + np.sqrt(noise_var) * np.random.normal(size=c.size)
return y

77
sw/python/utility/simulation/__init__.py
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@@ -1,77 +0,0 @@
"""Simulation package.
This package provides a way to easily define simulations in such a way that
they can be paused and resumed.
General Structure
=================
The package consists of 3 main components:
- The 'SimulationDeSerializer': Responsible for file IO
- The 'Simulator': Responsible for the actual simulating
- The 'SimulationManager': Delegates work to the DeSerializer and the
Simulator
The Simulator Class
===================
For each new simulating task, a new 'Simulator' must be defined. The
requirements for this class are the following:
- Must define the 'start_or_continue()', 'stop()' and
'get_current_results()' functions
- Must be picklable in order to store the simulation state
An example simulator could look as follows:
----------------------------------------------------------------
class SomeSimulator:
def __init__(self, num_iterations):
self._num_iterations = num_iterations
self._current_iter = 0
self._simulation_running = False
self._results = pd.DataFrame()
def _perform_iteration(self):
# Perform iteration and append results
...
def start_or_continue(self) -> None:
self._simulation_running = True
while self._simulation_running and (
self._current_iter < self._num_iterations):
self._perform_iteration()
def stop(self) -> None:
self._simulation_running = False
def get_current_results(self) -> pd.DataFrame:
return self._results
----------------------------------------------------------------
Usage
=====
To start a new simulation:
----------------------------------------------------------------
sim_mgr = SimulationManager(results_dir="results", saves_dir="saves")
sim = SomeSimulator(num_iterations=100)
sim_mgr.configure_simulation(simulator=sim, name='Some Simulation', \
column_labels=['label1', 'label2'])
sim_mgr.start()
----------------------------------------------------------------
To check for a previously interrupted simulation and continue:
----------------------------------------------------------------
sim_mgr = SimulationManager(results_dir="results", saves_dir="saves")
unfinished_sims = sim_mgr.get_unfinished()
if len(unfinished_sims) > 0:
sim_mgr.load_unfinished(unfinished_sims[0])
sim_mgr.simulate()
----------------------------------------------------------------
"""
from utility.simulation.management import SimulationManager, \
SimulationDeSerializer

239
sw/python/utility/simulation/management.py
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@@ -1,239 +0,0 @@
import json
import pandas as pd
import typing
import signal
import pickle
import os
from pathlib import Path
import platform
from datetime import datetime
import timeit
import collections.abc
from utility import misc
class SimulationDeSerializer:
"""Class responsible for file management, de- and serialization of
Simulator objects."""
def __init__(self, save_dir: str, results_dir: str):
self._saves_dir = save_dir
self._results_dir = results_dir
Path(self._saves_dir).mkdir(parents=True, exist_ok=True)
Path(self._results_dir).mkdir(parents=True, exist_ok=True)
def _get_savefile_path(self, sim_name):
return f"{self._saves_dir}/{misc.slugify(sim_name)}_state.pickle"
def _get_metadata_path(self, sim_name):
return f"{self._results_dir}/{misc.slugify(sim_name)}_metadata.json"
def _get_results_path(self, sim_name):
return f"{self._results_dir}/{misc.slugify(sim_name)}.csv"
def _read_metadata(self, sim_name) -> typing.Dict:
with open(self._get_metadata_path(sim_name), 'r',
encoding='utf-8') as f:
return json.load(f)
def _save_metadata(self, sim_name, metadata) -> None:
with open(self._get_metadata_path(sim_name), 'w+',
encoding='utf-8') as f:
json.dump(metadata, f, ensure_ascii=False, indent=4)
def unfinished_sim_present(self, sim_name: str):
"""Check if the savefile of a previously paused simulation is
present.
:param sim_name: Name
:return: True if a paused simulation with the given name is found
"""
return os.path.isfile(
self._get_savefile_path(sim_name)) and os.path.isfile(
self._get_metadata_path(sim_name))
# TODO: Make the directories configurable in the init function
def get_unfinished_sims(self) -> typing.List[str]:
"""Get a list unfinished simulations."""
save_files = [f for f in os.listdir(self._saves_dir) if
os.path.isfile(os.path.join(self._saves_dir, f))]
state_files = [f for f in save_files if f.endswith("_state.pickle")]
sim_slugs = [f.removesuffix("_state.pickle") for f in state_files]
sim_names = [self._read_metadata(slug)["name"] for slug in sim_slugs]
return sim_names
def remove_unfinished_sim(self, sim_name: str):
"""Remove the savefile of a previously paused simulation.
:param sim_name: Name of the simulation
"""
os.remove(self._get_savefile_path(sim_name))
# os.remove(self._get_metadata_path(sim_name))
def save_state(self, simulator: typing.Any, sim_name: str,
metadata: typing.Dict) -> None:
"""Save the state of a currently running simulation.
:param simulator: Simulator object
:param sim_name: Name of the simulation
:param metadata: Metadata to be saved besides the actual state
"""
# Save metadata
self._save_metadata(sim_name, metadata)
# Save simulation state
with open(self._get_savefile_path(sim_name), "wb") as file:
pickle.dump(simulator, file)
def read_state(self, sim_name: str) -> typing.Tuple[
typing.Any, typing.Dict]:
"""Read the saved state of a paused simulation.
:param sim_name: Name of the simulation
:return: Tuple of the form (simulator, metadata)
"""
# Read metadata
metadata = self._read_metadata(sim_name)
# Read simulation state
simulator = None
with open(self._get_savefile_path(sim_name), "rb") as file:
simulator = pickle.load(file)
return simulator, metadata
# TODO: Is the simulator object actually necessary here?
def save_results(self, simulator: typing.Any, sim_name: str,
metadata: typing.Dict) -> None:
"""Save simulation results to file.
:param simulator: Simulator object. Used to obtain the data
:param sim_name: Name of the simulation. Determines the filename
:param metadata: Metadata to be saved besides the actual simulation
results
"""
# Save metadata
self._save_metadata(sim_name, metadata)
# Save current results
simulator.current_results.to_csv(self._get_results_path(sim_name),
index=False)
def read_results(self, sim_name: str) -> typing.Tuple[
pd.DataFrame, typing.Dict]:
"""Read simulation results from file.
:param sim_name: Name of the simulation.
:return: Tuple of the form (data, metadata), where data is a pandas
dataframe and metadata is a dict
"""
# Read metadata
metadata = self._read_metadata(sim_name)
# Read results
results = pd.read_csv(self._get_results_path(sim_name))
return results, metadata
# TODO: Autosave simulation every so often
# TODO: Comment explaining what a Simulator class is
class SimulationManager:
"""This class only contains functions relating to stopping and
restarting of simulations (and storing of the simulation state in a
file, to be resumed at a later date).
All actual work is outsourced to a provided simulator class.
"""
def __init__(self, saves_dir: str, results_dir: str):
"""Construct a SimulationManager object.
:param saves_dir: Directory in which the simulation state of a paused
simulation should be stored
:param results_dir: Directory in which the results of the simulation
should be stored
"""
self._de_serializer = SimulationDeSerializer(saves_dir, results_dir)
self._simulator = None
self._sim_name = None
self._metadata = {"duration": 0}
self._sim_start_time = None
def _sim_configured(self) -> bool:
"""Check whether 'configure_simulation()' has been called."""
return (self._simulator is not None) and (
self._sim_name is not None) and (
self._metadata is not None)
def configure_simulation(self, simulator: typing.Any, name: str,
additional_metadata: dict = {}) -> None:
"""Configure a new simulation."""
self._simulator = simulator
self._sim_name = name
self._metadata["name"] = name
self._metadata["platform"] = platform.platform()
self._metadata.update(additional_metadata)
def get_unfinished(self) -> typing.List[str]:
"""Get a list of names of all present unfinished simulations."""
return self._de_serializer.get_unfinished_sims()
def load_unfinished(self, sim_name: str) -> None:
"""Load the state of an unfinished simulation form its savefile.
Warning: This function deletes the savefile after loading.
"""
assert self._de_serializer.unfinished_sim_present(sim_name)
self._sim_name = sim_name
self._simulator, self._metadata = self._de_serializer.read_state(
sim_name)
self._de_serializer.remove_unfinished_sim(sim_name)
# TODO: Metadata is being written twice here. Should save_results() also
# save the metadata?
def _exit_gracefully(self, *args) -> None:
"""Handler called when the program is interrupted. Pauses and saves
the currently running simulation."""
if self._sim_configured():
self._simulator.stop()
self._metadata["end_time"] = f"{datetime.now(tz=None)}"
self._metadata["duration"] \
+= timeit.default_timer() - self._sim_start_time
self._de_serializer.save_state(self._simulator, self._sim_name,
self._metadata)
self._de_serializer.save_results(self._simulator, self._sim_name,
self._metadata)
exit()
def simulate(self) -> None:
"""Start the simulation. This is a blocking call."""
assert self._sim_configured()
try:
self._sim_start_time = timeit.default_timer()
self._simulator.start_or_continue()
self._metadata["end_time"] = f"{datetime.now(tz=None)}"
self._metadata["duration"] \
+= timeit.default_timer() - self._sim_start_time
self._de_serializer.save_results(self._simulator, self._sim_name,
self._metadata)
except KeyboardInterrupt:
self._exit_gracefully()
def get_current_results(self) -> pd.DataFrame:
return self._simulator.current_results

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sw/python/utility/simulation/simulators.py
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import pandas as pd
import numpy as np
import typing
from tqdm import tqdm
from concurrent.futures import ProcessPoolExecutor, process, wait
from functools import partial
from multiprocessing import Lock
from utility import noise
# TODO: Fix ProximalDecoder_Dynamic
# from cpp_modules.cpp_decoders import ProximalDecoder_Dynamic as
# ProximalDecoder
def count_bit_errors(d: np.array, d_hat: np.array) -> int:
"""Count the number of wrong bits in a decoded codeword.
:param d: Originally sent data
:param d_hat: Received data
:return: Number of bit errors
"""
return np.sum(d != d_hat)
class HashableDict:
"""Class behaving like an immutable dict. More importantly it is
hashable and thus usable as a key type for another dict."""
def __init__(self, data_dict):
assert (isinstance(data_dict, dict))
for key, val in data_dict.items():
self.__dict__[key] = val
def __getitem__(self, item):
return self.__dict__[item]
def __str__(self):
return str(self.__dict__)
class GenericMultithreadedSimulator:
def __init__(self, max_workers=8):
self._format_func = None
self._task_func = None
self._task_params = None
self._max_workers = max_workers
self._results = {}
self._executor = None
@property
def task_params(self):
return self._task_params
@task_params.setter
def task_params(self, sim_params):
self._task_params = {HashableDict(iteration_params): iteration_params
for iteration_params in sim_params}
@property
def task_func(self):
return self._task_func
@task_func.setter
def task_func(self, func):
self._task_func = func
@property
def format_func(self):
return self._format_func
@format_func.setter
def format_func(self, func):
self._format_func = func
def start_or_continue(self):
assert self._task_func is not None
assert self._task_params is not None
assert self._format_func is not None
self._executor = ProcessPoolExecutor(max_workers=self._max_workers)
with tqdm(total=(len(self._task_params)), leave=False) as pbar:
def done_callback(key, f):
try:
pbar.update(1)
self._results[key] = f.result()
del self._task_params[key]
except process.BrokenProcessPool:
# This exception is thrown when the program is
# prematurely stopped with a KeyboardInterrupt
pass
futures = []
for key, params in list(self._task_params.items()):
future = self._executor.submit(self._task_func, params)
future.add_done_callback(partial(done_callback, key))
futures.append(future)
self._executor.shutdown(wait=True, cancel_futures=False)
def stop(self):
assert self._executor is not None, "The simulation has to be started" \
" before it can be stopped"
self._executor.shutdown(wait=True, cancel_futures=True)
@property
def current_results(self):
return self._format_func(self._results)
def __getstate__(self):
state = self.__dict__.copy()
state["_executor"] = None
return state
def __setstate__(self, state):
self.__dict__.update(state)
self._executor = ProcessPoolExecutor()

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sw/python/utility/test/__init__.py
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"""This package contains unit tests."""

72
sw/python/utility/test/test_proximal.py
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import unittest
import numpy as np
from decoders import proximal
class CheckParityTestCase(unittest.TestCase):
"""Test case for the check_parity function."""
def test_check_parity(self):
# Hamming(7,4) code
G = np.array([[1, 1, 1, 0, 0, 0, 0],
[1, 0, 0, 1, 1, 0, 0],
[0, 1, 0, 1, 0, 1, 0],
[1, 1, 0, 1, 0, 0, 1]])
H = np.array([[1, 0, 1, 0, 1, 0, 1],
[0, 1, 1, 0, 0, 1, 1],
[0, 0, 0, 1, 1, 1, 1]])
R = np.array([[0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 1]])
decoder = proximal.ProximalDecoder(H)
d1 = np.array([0, 1, 0, 1])
c1 = np.dot(np.transpose(G), d1) % 2
d2 = np.array([0, 0, 0, 0])
c2 = np.dot(np.transpose(G), d2) % 2
d3 = np.array([1, 1, 1, 1])
c3 = np.dot(np.transpose(G), d3) % 2
invalid_codeword = np.array([0, 1, 1, 0, 1, 1, 1])
self.assertEqual(decoder._check_parity(c1), True)
self.assertEqual(decoder._check_parity(c2), True)
self.assertEqual(decoder._check_parity(c3), True)
self.assertEqual(decoder._check_parity(invalid_codeword), False)
class GradientTestCase(unittest.TestCase):
"""Test case for the calculation of the gradient of the
code-constraint-polynomial."""
def test_grad_h(self):
"""Test the gradient of the code-constraint polynomial."""
# Hamming(7,4) code
G = np.array([[1, 1, 1, 0, 0, 0, 0],
[1, 0, 0, 1, 1, 0, 0],
[0, 1, 0, 1, 0, 1, 0],
[1, 1, 0, 1, 0, 0, 1]])
H = np.array([[1, 0, 1, 0, 1, 0, 1],
[0, 1, 1, 0, 0, 1, 1],
[0, 0, 0, 1, 1, 1, 1]])
R = np.array([[0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 1]])
x = np.array([1, 2, -1, -2, 2, 1, -1]) # Some randomly chosen vector
expected_grad_h = np.array(
[4, 26, -8, -36, 38, 28, -32]) # Manually calculated result
decoder = proximal.ProximalDecoder(H)
grad_h = decoder._grad_h(x)
self.assertEqual(np.array_equal(grad_h, expected_grad_h), True)
if __name__ == "__main__":
unittest.main()

55
sw/python/utility/test/test_soft_decision.py
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import unittest
import numpy as np
from decoders import maximum_likelihood
class CodewordGenerationTestCase(unittest.TestCase):
def test_codeword_generation(self):
"""Test case for data word and code word generation."""
# Hamming(7,4) code
G = np.array([[1, 1, 1, 0, 0, 0, 0],
[1, 0, 0, 1, 1, 0, 0],
[0, 1, 0, 1, 0, 1, 0],
[1, 1, 0, 1, 0, 0, 1]])
H = np.array([[1, 0, 1, 0, 1, 0, 1],
[0, 1, 1, 0, 0, 1, 1],
[0, 0, 0, 1, 1, 1, 1]])
decoder = maximum_likelihood.MLDecoder(G, H)
expected_datawords = np.array([[0, 0, 0, 0],
[0, 0, 0, 1],
[0, 0, 1, 0],
[0, 0, 1, 1],
[0, 1, 0, 0],
[0, 1, 0, 1],
[0, 1, 1, 0],
[0, 1, 1, 1],
[1, 0, 0, 0],
[1, 0, 0, 1],
[1, 0, 1, 0],
[1, 0, 1, 1],
[1, 1, 0, 0],
[1, 1, 0, 1],
[1, 1, 1, 0],
[1, 1, 1, 1]])
expected_codewords = np.array([[0, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 1, 0, 0, 1],
[0, 1, 0, 1, 0, 1, 0],
[1, 0, 0, 0, 0, 1, 1],
[1, 0, 0, 1, 1, 0, 0],
[0, 1, 0, 0, 1, 0, 1],
[1, 1, 0, 0, 1, 1, 0],
[0, 0, 0, 1, 1, 1, 1],
[1, 1, 1, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 1],
[1, 0, 1, 1, 0, 1, 0],
[0, 1, 1, 0, 0, 1, 1],
[0, 1, 1, 1, 1, 0, 0],
[1, 0, 1, 0, 1, 0, 1],
[0, 0, 1, 0, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 1]])
self.assertEqual(np.array_equal(decoder._datawords, expected_datawords), True)
self.assertEqual(np.array_equal(decoder._codewords, expected_codewords), True)

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sw/python/utility/test/test_utility.py
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import unittest
import numpy as np
from utility import noise, codes
# TODO: Rewrite tests for new SNR calculation
class NoiseAmpFromSNRTestCase(unittest.TestCase):
"""Test case for noise amplitude calculation."""
def test_get_noise_amp_from_SNR(self):
SNR1 = 0
SNR2 = 3
SNR3 = 20
SNR4 = -20
var1 = noise.get_noise_variance_from_SNR(SNR1, n=8, k=8)
var2 = noise.get_noise_variance_from_SNR(SNR2, n=8, k=8)
var3 = noise.get_noise_variance_from_SNR(SNR3, n=8, k=8)
var4 = noise.get_noise_variance_from_SNR(SNR4, n=8, k=8)
self.assertEqual(var1, 1 * 0.5)
self.assertAlmostEqual(var2, 0.5 * 0.5, places=2)
self.assertEqual(var3, 0.01 * 0.5)
self.assertEqual(var4, 100 * 0.5)
class CodesTestCase(unittest.TestCase):
"""Tests relating to the 'codes' utilities."""
def test_get_systematic_H(self):
# Hamming(7,4) code
G = np.array([[1, 0, 0, 0, 0, 1, 1],
[0, 1, 0, 0, 1, 0, 1],
[0, 0, 1, 0, 1, 1, 0],
[0, 0, 0, 1, 1, 1, 1]])
expected_H = np.array([[0, 1, 1, 1, 1, 0, 0],
[1, 0, 1, 1, 0, 1, 0],
[1, 1, 0, 1, 0, 0, 1]])
H = codes.get_systematic_H(G)
self.assertEqual(np.array_equal(expected_H, H), True)
if __name__ == '__main__':
unittest.main()

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sw/python/utility/test/test_visualization.py
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import unittest
from utility import visualization
class NumRowsTestCase(unittest.TestCase):
def test_get_num_rows(self):
"""Test case for number of row calculation."""
num_rows1 = visualization._get_num_rows(num_graphs=4, num_cols=3)
expected_rows1 = 2
num_rows2 = visualization._get_num_rows(num_graphs=5, num_cols=2)
expected_rows2 = 3
num_rows3 = visualization._get_num_rows(num_graphs=4, num_cols=4)
expected_rows3 = 1
num_rows4 = visualization._get_num_rows(num_graphs=4, num_cols=5)
expected_rows4 = 1
self.assertEqual(num_rows1, expected_rows1)
self.assertEqual(num_rows2, expected_rows2)
self.assertEqual(num_rows3, expected_rows3)
self.assertEqual(num_rows4, expected_rows4)

75
sw/python/utility/visualization.py
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import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import typing
from itertools import chain
import math
def _get_num_rows(num_graphs: int, num_cols: int) -> int:
"""Get the minimum number of rows needed to show a certain number of
graphs, given a certain number of columns.
:param num_graphs: Number of graphs
:param num_cols: Number of columns
:return: Number of rows
"""
return math.ceil(num_graphs / num_cols)
# TODO: Handle number of graphs not nicely fitting into rows and columns
def plot_BERs(title: str,
data: typing.Sequence[
typing.Tuple[str, pd.DataFrame, typing.Sequence[str]]],
num_cols: int = 3) -> plt.figure:
"""This function creates a matplotlib figure containing a number of plots.
The plots created are logarithmic and the scaling is adjusted to be
sensible for BER plots.
:param title: Title of the figure
:param data: Sequence of tuples. Each tuple corresponds to a new plot and
is of the following form: [graph_title, pd.Dataframe, [line_label_1,
line_label2, ...]]. Each dataframe is assumed to have an "SNR" column
that is used as the x axis.
:param num_cols: Number of columns in which the graphs should be
arranged in the resulting figure
:return: Matplotlib figure
"""
# Determine layout and create figure
num_graphs = len(data)
num_rows = _get_num_rows(num_graphs, num_cols)
fig, axes = plt.subplots(num_rows, num_cols,
figsize=(num_cols * 4, num_rows * 4),
squeeze=False)
fig.suptitle(title)
fig.subplots_adjust(left=0.1,
bottom=0.1,
right=0.9,
top=0.9,
wspace=0.3,
hspace=0.4)
axes = list(chain.from_iterable(axes))[
:num_graphs] # Flatten the 2d axes array
# Populate axes
for axis, (graph_title, df, labels) in zip(axes, data):
column_names = [column for column in df.columns.values.tolist() if
not column == "SNR"]
for column, label in zip(column_names, labels):
sns.lineplot(ax=axis, data=df, x="SNR", y=column, label=label)
axis.set_title(graph_title)
axis.set(yscale="log")
axis.set_xlabel("SNR")
axis.set_ylabel("BER")
axis.set_yticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 10e0])
axis.legend()
return fig